1 Introduction
As the concept of Data Science has grown, so have all the areas that are impacted by it, having so many areas that need this field means that there needs to be experts in a variety of skills to provide a quality service that a costumer might ask for. It is because of this that the education on these areas has become so important. Here in Georgetown in the Data Science and Analytics program the options to choose when it comes to these fields is quite big, electives that are open for students focus on a variety of areas, giving the students the chance to choose a path that they feel interested in to learn more about. Since the job listings available combine with the classes that second year students might take, the goal for this project is to create a visualization document that users can use to make the correct choice when it comes to choosing their electives.
2 Obtaining our data
The first step in this analysis is understanding our data, we have obtained 40 job listings in 9 areas of data science and 6 in the last one. These areas are: Data Analyst, Data Scientist,Deep Learning,Block chain,Machine Learning,Big Data,NLP, Neural Networks, Reinforcement Learning and Time series. It seems like the time series topic had problems when pulling job listings, which is why we only obtained 6 from this field. In the figure below we will be able to observe the websites from which these jobs were obtained, showing the websites that would be more benefitial for us to look into depending on the area that we want to focus on.
As we are able to observe thanks to the graph, depending on the area that we are interested in, it would be better for us to focus on different websites that have job listings, some of them include Linked inn which seems to be a common occurence in all of thems, but other topics such as deep learning or neural networks seem to have more listings in places such as AngelList and Upwork, which tells us that is better to keep our options open.
3 Location of Job listings
One of the aspects of a job listing that often affects the decision of the person to apply or not to is the location of the job, since relocation might not be an option for some people it is important to take into consideration what area of data science will allow them to find the biggest amount of jobs on their desired location. Based on this we created a map showing how the different areas available have different locations, which we will show below.
As we can observe based on the map, different areas on United States tend to have listings for different topics in data science, based on this students might be able to make a decision on the elective they would like to take depending on what part of the United States they would prefer to stay in; such as Big Data if they wanted to stay in the DC area, machine learning, nlp and reinforcement learning if their goal is to move to California, or blockchain if they do not have a set preference since it seems to be needed in various parts of USA.
4 Scheduling type
Once Location has been sorted, it is also time to explore the different scheduling types that we might encounter when looking for job listings and how they might affect our choices. Schedules can be very important when applying for jobs, which is why it would be helpful to observe which areas have more listings with the schedule type that we might prefer. To do so we created a bar graph showing the count of schedule types in each of the areas we have information on, which will give us a better idea on the path we might want to take.